Numeracy and Literacy Independently Predict Patients’ Ability to Identify Out-of-Range Test Results


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Consumer Patient Education and Shared-Decision Making 


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Original Paper

Numeracy and Literacy Independently Predict Patients’ Ability to Identify Out-of-Range Test Results

Brian J Zikmund-Fisher1,2,3,4, PhD;
Nicole L Exe3, MPH;
Holly O Witteman5,6,7, PhD

1Department of Health Behavior and Health Education, University of Michigan, Ann Arbor, MI, United States
2Department of Internal Medicine, University of Michigan, Ann Arbor, MI, United States
3Center for Bioethics and Social Sciences in Medicine, University of Michigan, Ann Arbor, MI, United States
4Risk Science Center, University of Michigan, Ann Arbor, MI, United States
5Department of Family and Emergency Medicine, Faculty of Medicine, Université Laval, Québec City, QC, Canada
6Office of Education and Continuing Professional Development, Faculty of Medicine, Université Laval, Québec City, QC, Canada
7Research Centre of the CHU de Québec, Québec City, QC, Canada

Corresponding Author:
Brian J Zikmund-Fisher, PhD

Department of Health Behavior and Health Education
University of Michigan
1415 Washington Heights
Ann Arbor, MI, 48109
United States
Phone: 1 7349369179
Fax: 1 7347637379
Email:


1], and already exists in some large health care systems. Strong federal incentives supporting adoption of electronic medical record systems will likely significantly increase the availability of patient portals in the future.

Patients use such systems to view medical test results and value being able to do so [2-4]. Direct patient use of test data is consistent with trends toward patient-centered approaches to care, patient engagement, and the medical home concept, all of which encourage greater patient involvement in both medical decision making and health self-management [5-7]. In that manner, patient access to such health data promotes a transfer of some of the responsibility for health management from care providers to the patients themselves [8]. Patient access is also congruent with the trend for people to actively gather, manage, and analyze their personal data (eg, the “quantified self” movement) [9]. Perhaps most importantly, there is an ethical imperative to provide easy access to patients who want it [10].

Patients want to be notified of laboratory test results, regardless of whether the findings were normal or abnormal [11], because failures to inform patients of test results are unfortunately all too common, even for abnormal or otherwise actionable test results [12]. Direct access enables patients to seek out their results by themselves, thereby providing a second opportunity for identifying actionable results and preventing unnecessary harm.

Test result data can also enable patients to better prepare for clinic visits by focusing their attention on test results that are abnormal or of concern. This knowledge could lead patients to prepare questions or seek out relevant information before the visit. Such preparation benefits patients, but it also benefits the health care system by making visits more efficient [1].

Patients can also use test results to improve self-management of their current health conditions [1,13]. For example, a person with diabetes could both assess her current status and identify long-term trends in her blood glucose control. She could use such data to determine whether her current health management efforts (eg, behavior programs, medications) are working. Such information offers the potential to increase patient activation and the likelihood of engaging in particular treatment or health behaviors [1].

Achievement of these potential benefits, however, requires patients to perform a simple, yet critical, task: to be able to correctly identify which test results are out of range (ie, outside the reference ranges) from the (usually) much larger set of data provided. Unfortunately, there are several reasons to suspect that many patients will have difficulty with this task when the test results are displayed in the tabular format currently used in many interface designs.

First, many patients have limited health literacy, which inhibits their ability to interpret the health information they read and use that information to manage their health [14-16]. For example, low health literacy is associated with less knowledge about the medications one is taking [17], being less able to read and understand medication labels [18], unintentional nonadherence to hospital discharge instructions [19], and increased mortality [20,21]. Health literacy affects patient use of laboratory test results in 2 ways. First, lack of attention to issues of health literacy when designing patient portals limits the accessibility of such tools and, therefore, limits their impact among those who might most benefit from them. Even restricting analysis to people with Internet access, lower health literacy is associated with a lower likelihood of logging into a patient portal in the first place [22]. Indeed, patient portal use is lower among the more vulnerable populations [23]. Second, health literacy affects patients’ abilities to gather background and contextual information (eg, about what a test is, what values are normal or concerning) necessary to cognitively evaluate the meaning of a test result in relation to their health.

Second, many patients also have lower numeracy skills (ie, poor ability to use and draw meaning from numbers) [24,25]. Although some measures of health literacy (eg, Test of Functional Health Literacy in Adults, TOFHLA) include assessments of what is variably termed numerical ability or quantitative literacy, there is growing evidence that numeracy is a distinct construct that is particularly relevant to data interpretation tasks. Numeracy predicts people’s ability to read nutrition labels, calculate medication dosages, maintain anticoagulation control, and maintain glycemic control better than measures of health literacy do [26-29]. Patients with lower numeracy skills may lack the capacity to interpret test outcome data in some current presentations. In addition, numeracy appears necessary for people to develop emotional responses to data [30]. This is problematic given the large amount of theoretical and experimental evidence that emotions are both integral to risk perceptions and necessary for effective decision making [31-34]. As a result, less numerate patients are unlikely to know how to use medical test results if they cannot get a feeling of “goodness” or “badness” from the data [30].

Without careful design that attends to issues of health literacy and numeracy, presentations of laboratory test results (whether in patient portals or via a clinician’s office) could be of little use to less literate and numerate patients. Although some initiatives have used cues such as color to help patients identify out-of-range values [35], often laboratory results are shown in the same tabular format that is provided to clinicians. These tables present a dozen or more tests simultaneously, usually labeled with unfamiliar abbreviations, reported in unfamiliar units, and lacking guidance as to whether higher numbers represent more positive or negative outcomes. Unfortunately, less numerate people have particular difficulty identifying decision-relevant information out of larger sets of data [36]. Therefore, the sheer volume of information available through patient portals is particularly challenging for the less numerate [8].

We designed an experimental study to assess the degree that adults, especially those with lower numeracy and/or lower health literacy, are able or not able to identify out-of-range values in prototypical medical test result displays. Participants viewed multiple panels of test results typical of what would be ordered for ongoing management of a person with type 2 diabetes and were asked to (1) identify all values outside the reference range, (2) assess the degree of blood glucose control represented by those results, and (3) identify whether they would call their doctor regarding these results. To test patient sensitivity to variations in test results, we experimentally varied 2 factors: (1) hemoglobin A1c levels were mildly or moderately elevated and (2) other test results were within or outside their reference ranges. To enable assessment of the role of numeracy and health literacy skills on people’s ability to complete these tasks accurately, all participants completed validated measures of both constructs.

Participants

We recruited a stratified random sample of US adults aged 40-70 years from a panel of Internet users administered by Survey Sampling International (SSI, Shelton, CT, USA), which recruits panel members through various opt-in methods. To ensure demographic diversity (although not representativeness) and offset variations in response rates, we drew subsamples by both age and race (thereby approximating the distributions of these characteristics in the US population). We also drew separate subsamples by experience with diabetes: We specified that approximately half of completed surveys be from panel participants who had previously indicated that they had diabetes (and hence might have had greater knowledge about hemoglobin A1c tests) and half from people without personal experience with diabetes (who might be more similar to newly diagnosed patients). The number of email invitations in each subsample was dynamically adjusted until quotas were achieved.

Selected panel members received email invitations with a personalized link (tracked to prevent duplicates) and nonresponders received 1 reminder email. Those who clicked on the link then viewed an introductory page that provided information about the estimated length of the survey (10 to 15 minutes), the purpose of the study, and affiliation and contact information for the investigators before taking the participant to the main study materials. We recruited for a 2-week period in January 2013. On completion, participants were entered into instant-win contests and regular draws administered by SSI for modest prizes.

Design

Participants were asked to imagine that they were diagnosed with type 2 diabetes, had been maintaining good blood glucose control with a previous hemoglobin A1c test result of 6.8%, and had an explicit goal of maintaining hemoglobin A1c values below 7%. Participants were then asked to imagine that they were viewing the results of a set of blood tests (complete blood cell count, CBC; hemoglobin A1c; and renal panel) that had been ordered between doctor’s visits. Following the format currently implemented in the patient portal of a major academic medical center, all tables showed test values, standard ranges, and units, but did not show indicators for high or low values (the medical center includes high/low indicators in clinician interfaces but omits them from the patient interface). As shown in Figure 1, all tests were presented on a single page grouped by panel per standard practice.

We manipulated the test results shown in a 2×2 factorial design. All participants viewed results that showed that hemoglobin A1c was elevated above the standard range (reported as 3.8%-6.4%). We randomly varied the degree of A1c elevation by randomizing participants to view a hemoglobin A1c result of either 7.1% or 8.4%. Thus, both values should be identified as out of range, but only the 8.4% value is sufficiently high (and a large enough change from the previous value) to potentially warrant more timely attention. In addition, we independently varied whether all other reported results were within standard ranges (single deviation condition) or whether multiple results were out of range (multiple deviations condition). Participants in the multiple deviations condition saw tables with out-of-range values for white blood cell (WBC) count, platelet count, mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), neutrophil %, lymphocyte %, monocyte %, absolute neutrophil count, and serum glucose. These values were either elevated or reduced to be consistent with a temporary viral infection.

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